Core Concepts
Spiking Neural Networks (SNNs) offer energy efficiency through event-driven computation, with the proposed single-spike phase coding enabling accurate ANN-to-SNN conversion.
Abstract
The content discusses the conversion of pre-trained Artificial Neural Networks (ANNs) to Spiking Neural Networks (SNNs) for improved energy efficiency. It introduces a single-spike phase coding method to minimize conversion loss and maintain accuracy. The paper details the encoding schemes, neuron models, weight normalization techniques, and error reduction methods in one-spike SNNs. Experimental results demonstrate high accuracy with reduced timesteps compared to prior works, emphasizing energy efficiency.
Structure:
- Introduction to ANNs and SNNs' energy efficiency.
- Challenges in training SNNs due to spike-based data transfer.
- Proposal of single-spike phase coding for accurate ANN-to-SNN conversion.
- Detailed explanation of activation encoding, neuron models, and weight normalization.
- Analysis of conversion errors and manipulation of base Q for improved accuracy.
- Experimental results on CIFAR and ImageNet datasets showcasing accuracy and energy efficiency improvements.
Stats
SNNのエネルギー効率は、ANNに比べて4.6〜17.3倍向上しています。
提案された方法では、平均で推論精度が0.58%向上しました。
グラフ畳み込みネットワーク(GCN)をSNNに変換することができました。
Quotes
"Most importantly, the energy efficiency of our SNN improves by 4.6∼17.3× compared to the ANN baseline."